<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/3de54014865b4cb7aa59b87ec940626d&quot; frameborder=&quot;0&quot; width=&quot;1670&quot; height=&quot;1252&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1252</height><width>1670</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1252</thumbnail_height><thumbnail_width>1670</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3de54014865b4cb7aa59b87ec940626d-a4aca5b9f41f4f9e.gif</thumbnail_url><duration>656.1938</duration><title>Overview of HL7 Data Ingestion Pipeline Project</title><description>In this video, I provide an overview of my HL7 data ingestion project, which currently features a basic pipeline for processing HL7 messages, with plans to extend it to CCDA and X12. The pipeline includes a producer generating synthetic data, a consumer for validation, and a transformer converting HL7 to FHIR using Microsoft&apos;s Fire Converter. I&apos;ve integrated Grafana and Prometheus for metrics collection and alerting, and Kafka serves as the message broker. I encourage viewers to explore the code and provide feedback on the validation and QA processes, as well as any potential improvements. Your insights will be invaluable as I continue to develop this project.</description></oembed>